CONICET | Buscador de Institutos y Recursos Humanos

Multimodel inference in social and environmental sciences. Professionals of the social andenvironmental sciences must solve problems (answer questions) based on data sampling and analyses.Commonly, all professionals face similar challenges: they need to take decisions on a population (e.g., all the treesof a region), but only have data from a sample (some trees of that region). A key tool in this process is to proposepopulation models for the response variable (tree growth as a function of tree age and climatic conditions) andthen use model predictions to take decisions (e.g., when to cut trees according to climatic conditions). In thispaper we discuss how to propose, estimate, and select models of a population based on sampling data. Weput special emphasis in proposing several alternative models (hypotheses) to solve one problem (e.g., differenttree growth functions for age), which must be proposed before data sampling, including a null model (treegrowth does not depend on tree age or climatic conditions). Models guide us on how data must be sampledfor a valid contrast (growth measurements in trees of different age and under contrasting climates). Then, theAkaike information criterion (AIC) can be employed to sort the most parsimonious models, selecting thosewith the best goodness of fit (likelihood) and the lowest number of parameters (model complexity). Along thetext, we introduce basic notions of multimodel inference and discuss common user mistakes. We provide realexamples, and share their data and the analyses code in R, a free and open source software. In addition to beuseful to professionals from different sciences, we expect our paper to promote the teaching of multimodelinference in graduate courses.